Analysis for Article
Powering Responsible AI with High-Quality Real-World Data: The S-RACE Platform
The S-RACE platform, a secure, cloud-based solution developed in partnership with Microsoft and Porini, addresses critical data quality and governance challenges in healthcare AI. It systematically transforms raw hospital data into high-quality, research-grade evidence through an end-to-end pipeline, including on-premises anonymisation, NLP-driven extraction, and standardisation to FHIR format. Populated with data from over 31,000 patients and powering 19 research projects, S-RACE demonstrates comparable performance to manually curated data, accelerating the clinical adoption of responsible AI.
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Faster Data Curation
Deep Analysis & Enterprise Applications
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S-RACE Data Pipeline
On-Premises Anonymisation
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Secure Cloud Transfer
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NLP & FHIR Standardisation
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Curated Data Foundation
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Responsible AI Development
Governance and Compliance
S-RACE is underpinned by a robust governance model aligned with ISO 42001 and the EU AI Act, ensuring data quality, risk management, and human oversight. This commitment to ethical and safe AI development is integral to its design, from data pseudonymisation to model deployment. The platform employs a hybrid data quality model, combining expert-driven evaluation with an automated pre-processing workflow, reinforcing its 'privacy by design' approach.
Current Patient Count
31,276Total Unique PatientsThe platform currently serves 19 clinical research projects across diverse domains including oncology, cardiology, and diabetes, demonstrating its technical scalability and commitment to multidisciplinary research.
Kidney Cancer Mortality Prediction
Challenge: Lack of scalable, high-quality RWD for prognostic models in ccRCC.
Solution: Utilized S-RACE for automated RWD processing and AI model development.
Impact: Comparable performance to manually curated data, identifying novel prognostic variables and speeding up research.
In a study predicting cancer-specific mortality in non-metastatic clear cell renal cell carcinoma (ccRCC), models trained on S-RACE's automatically processed RWD performed comparably to those trained on manually curated data, validating the platform's core capability to generate high-quality, research-grade evidence. This project leveraged both traditional gold standard eCRF data and raw, unstructured RWD, demonstrating the platform's ability to extract novel prognostic variables.
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Responsible AI Development
The S-RACE platform supports responsible AI development by integrating explainability techniques (XAI) like SHAP to make model predictions interpretable. It also adheres to governance standards such as ISO 42001 and the EU AI Act, ensuring that AI models are not only accurate but also trustworthy and equitable. The platform provides a robust framework for managing risks, ensuring fairness, and addressing potential biases, which is critical for clinical adoption.
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Your AI Implementation Roadmap
Our structured approach ensures a smooth, secure, and impactful AI integration into your enterprise workflows.
01 Discovery & Strategy
Comprehensive assessment of your current infrastructure, data landscape, and business objectives to define a tailored AI strategy.
02 Data Foundation & Curation
Secure, privacy-preserving data ingestion, anonymisation, and standardization into a high-quality, research-grade dataset.
03 Model Development & Validation
Building, training, and rigorously validating AI models using responsible AI principles, including explainability and bias detection.
04 Deployment & Integration
Seamless integration of validated AI models into your existing clinical and operational systems with continuous monitoring.
05 Continuous Optimization
Ongoing performance monitoring, recalibration, and enhancement of AI solutions to adapt to evolving needs and data.
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